Vision-Language-Action (VLA) model plays a crucial role in embodied decision making. While practical deployment requires fast inference under limited onboard computation, a full forward pass through the vision-language model makes such deployment challenging. To address this issue, existing methods typically employ lightweight techniques to compress the backbone. However, these information-lossy methods degrade spatial representations for action generation. In contrast, rate-distortion principles aim to reduce computation while retaining control-sufficient information. Inspired by this insight, we introduce Effective-Edge Flow, an action-aligned attribution measure that quantifies the marginal contribution of token interactions across network depth. This analysis reveals a consistent depth asymmetry, with visual evidence dominating early layers and linguistic reasoning sustaining task-relevant influence into deeper layers. Building on this structure, we propose MoDeVLA, the first rate-distortion driven efficient VLA model that performs token-wise depth allocation via Mixture-of-Depth Conditioning and integrates shallow visual-spatial with deep textual-logical features for action conditioning. Extensive real-robot evaluations across 20 tasks and multiple embodiments demonstrate that MoDeVLA preserves task performance while reducing latency by about 38% and FLOPs by 86% on edge device NVIDIA Jetson Orin, highlighting its strong ability for embodied systems deployment.
Weiying Xie, Qingcheng Zeng, Zihan Meng et al.· Proceedings of the 32nd ACM...· 0 citations
Vision-Language-Action (VLA) model plays a crucial role in embodied decision making. While practical deployment requires fast inference under limited onboard computation, a full forward pass through the vision-language model makes such deployment challenging. To address this issue, existing methods typically employ lightweight techniques to compress the backbone. However, these information-lossy methods degrade spatial representations for action generation. In contrast, rate-distortion principles aim to reduce computation while retaining control-sufficient information. Inspired by this insight, we introduce Effective-Edge Flow, an action-aligned attribution measure that quantifies the marginal contribution of token interactions across network depth. This analysis reveals a consistent depth asymmetry, with visual evidence dominating early layers and linguistic reasoning sustaining task-relevant influence into deeper layers. Building on this structure, we propose MoDeVLA, the first rate-distortion driven efficient VLA model that performs token-wise depth allocation via Mixture-of-Depth Conditioning and integrates shallow visual-spatial with deep textual-logical features for action conditioning. Extensive real-robot evaluations across 20 tasks and multiple embodiments demonstrate that MoDeVLA preserves task performance while reducing latency by about 38% and FLOPs by 86% on edge device NVIDIA Jetson Orin, highlighting its strong ability for embodied systems deployment.
Weiying Xie, Qingcheng Zeng, Zihan Meng et al.· Proceedings of the 32nd ACM...· 0 citations
LoSA is a training-free sparse-attention method that fixes a retained-mass threshold of 99% rather than a sparsity ratio: it measures exact block attention masses at one early dense step, keeps, for each head and query block, the smallest key/value block set meeting the threshold, and reuses the frozen block indices for all remaining steps.
Enhuai Liu, Yunke Wang, Yutong Wang et al.· 0 citations
The real-robot benchmark demonstrates that StellaVLA can use both human/robot demos and human-to-robot (XR) demos as in-context structured demonstration to help VLA model adapt to OOD tasks.
Siyu Xu, Yunke Wang, Zijian Wang et al.· 0 citations
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